A virtual ecological approach to modeling uncertainties in paleoecology

Quinn Asena

University of auckland, Cary Institute

George Perry

University of auckland

Janet Wilmshurst

Manaaki Whenua - Landcare Research

2025-11-17

Uncertainties in palaeoecology


The problem


Proxy data are the product of multiple sources of uncertainty


  • Environmental processes
    • bioturbation, taphonomy, variable sedimentation rates…
  • Field and laboratory methods
    • core collection methods, sub-sampling strategy, pollen counting…
  • Data processing methods
    • age-depth modelling, interpolation…

The question: is the past recoverable from the data?


Why it matters


  • Palaeoecology moving from descriptive to quantitative
  • Palaeoecology to inform the future requires robust statistical approaches
  • Advances in lab methods, data availability, and statistics are making more inferences possible

What we can do about it


  • One method to assess uncertainties is in simulation
  • We use pseudoproxy modelling / Virtual ecology

Approach


  • Simulate core samples containing proxies mimicing the statistical properties of empirical data
  • Simulate process and observer error that affect the data
  • Assess how statistical inferences are affected by process and observer error

Key concepts

Virtual ecology

Virtual ecology is a framework for assessing sampling and analytical methods in simulation consisting of:

  1. an ecological model that generates synthetic data

    1a. a degradation model

  2. a simulated observational process (a sampling model) that samples the synthetic data

  3. an analytical process or statistical model applied to both sets of data

  4. an assessment of the results

Virtual ecology and empirical ecology


Perfect knowledege, imperfect world

  • Known drivers and responses

  • Known environmental and observational processes

  • Advantage of benchmark/control data

  • Advantage of replication

  • Able to systematically introduce uncertainty

Perfect world, imperfect knowledge

  • Sampled data with no benchmark/control

  • Advantage of being reality

Proxy system modelling


The process by which environmental change is recorded as an observable signal in an archive:

  1. Environmental drivers (e.g., climatic variability)
  2. A sensor (a physical, biological or chemical component of the system that responds to the environmental drivers)
  3. An archive (the medium in which the response of the sensor is recorded such as a lake sediment)
  4. Observations drawn from the archive

Pseudoproxy experiments


Borrowing the term “pseudoproxies” from climatology:

  • Pseudoproxies are simulated data or modified observational data
  • Mimic the statistical properties of empirical data
  • Pseudoproxy experiments are similar to virtual ecology

Building the model

Let’s follow a singe replicate case-study

Simulating pseudoproxies


We set out to:

  • Represent multiple interacting drivers
  • Include underlying ecological dynamics that can undergo community turnover
  • Generate a multi-species pseudoproxy
  • Recreate core formation processes of accumulation rates and time-span
  • Virtually recreate the observational processes

Simulating pseudoproxies

Ruining pseudoproxies

Extending the proxy system model framework


  • Included a degradation (sub-)model to represent environmental processes

“Error-free” to degraded and sub-sampled

Example of 20/200 randomly selected species

Degraded and sub-sampled pseudoproxies

Analysing the outputs

Analyses


Ok, now we have generated the data, let’s analyse it. Two analyses:

  • Fisher Information
  • Principal curves

Demonstrating two scenarios with different driving environments.

Analyses visualised

Recap!


  1. Environmental driver patterns over time (environment model)
  2. Species that respond to the drivers (sensor model – pseudoproxies)
  3. Core representation: accumulation rate and time-span (archive model)
  4. Core mixing (degradation model)
  5. Sampling and counting process (observation model)
  6. Analyse the pseudoproxies (assessment – Virtual Ecology)

Pervious slides followed:

  • 1 scenario
    • we simulated four different driving environments
  • 1 replicate
    • we simulated 30 replicates to account for stochasticity

Question time

Extending to multiple scenarios and replicates


Each replicate results in 1210 datasets from the ‘error-free’ to the most uncertain 😱.


Across replicates for each of the 1210 datasets:

  1. extract features from the FI and PrC

    • feature analysis reduces the FI and PrC to one dimension

  2. calculate the distance between each dataset from the ‘error-free’ to the most uncertain

  3. make cool visulisations!

Think of it like this

References

Asena, Quinn, George L. W. Perry, and Janet M. Wilmshurst. 2025. “Information Loss in Palaeoecological Data from Process and Observer Error.” EGUsphere, March, 1–31. https://doi.org/10.5194/egusphere-2024-3845.
Asena, Quinn, George LW Perry, and Janet M Wilmshurst. 2024. “Is the Past Recoverable from the Data? Pseudoproxy Modelling of Uncertainties in Palaeoecological Data.” The Holocene, 09596836241247304.
Blaauw, Maarten, K. D. Bennett, and J. Andrés Christen. 2010. “Random Walk Simulations of Fossil Proxy Data.” The Holocene 20 (4): 645–49. https://doi.org/10.1177/0959683609355180.
Evans, M. N., S. E. Tolwinski-Ward, D. M. Thompson, and K. J. Anchukaitis. 2013. “Applications of Proxy System Modeling in High Resolution Paleoclimatology.” Quaternary Science Reviews 76 (September): 16–28. https://doi.org/10.1016/j.quascirev.2013.05.024.
Mann, Michael E., and Scott Rutherford. 2002. “Climate Reconstruction Using ‘Pseudoproxies’.” Geophysical Research Letters 29 (10): 139-1-139-4. https://doi.org/10.1029/2001GL014554.
Williams, John W., Jessica L. Blois, and Bryan N. Shuman. 2011. “Extrinsic and Intrinsic Forcing of Abrupt Ecological Change: Case Studies from the Late Quaternary.” Journal of Ecology 99 (3): 664–77. https://doi.org/10.1111/j.1365-2745.2011.01810.x.
Zurell, Damaris, Uta Berger, Juliano S. Cabral, Florian Jeltsch, Christine N. Meynard, Tamara Münkemüller, Nana Nehrbass, et al. 2010. “The Virtual Ecologist Approach: Simulating Data and Observers.” Oikos 119 (4): 622–35. https://doi.org/10.1111/j.1600-0706.2009.18284.x.